In [1]:
import sys
import os
sys.path.insert(0, os.path.abspath('../'))
sys.path.insert(0, os.path.abspath('../../'))
sys.path.insert(0, os.path.abspath('/home/hm-tlacherm/qlm_notebooks/notebooks_1.2.1/notebooks/master_thesis_qaoa/'))
sys.path.insert(0, os.path.abspath('/home/hm-tlacherm/qlm_notebooks/notebooks_1.2.1/notebooks/master_thesis_qaoa/ibm/'))
In [2]:
import numpy as np

import qiskit
provider = qiskit.IBMQ.load_account()
from qiskit import Aer
from qiskit.utils import QuantumInstance
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit.algorithms import QAOA
from shared.QiskitMaxcut import *
from ibm.ibm_parameters import *

from matplotlib import pyplot as plt
%matplotlib inline

from ibm_landscape_processes import *

ibmqfactory.load_account:WARNING:2021-08-20 18:30:22,239: Credentials are already in use. The existing account in the session will be replaced.
In [3]:
%load_ext autoreload
%autoreload 2
In [4]:
# ---- Define graph and MaxCut ----
graph = generate_butterfly_graph(with_weights=False)
max_cut = Maxcut(graph)
max_cut_qubo = max_cut.to_qubo()
max_cut.draw()
In [5]:
# ---- Define step size and gamma, beta values ----
step_size = 0.1
a_gamma = np.arange(0, np.pi, step_size)
b_beta = np.arange(0, np.pi, step_size)
In [6]:
gammas, betas = np.meshgrid(a_gamma, b_beta)
In [7]:
# ---- execute for all values QAOA and get result matrix  ----
landscape = run_all(gammas, betas, max_cut)

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In [8]:
print(landscape)
plt.matshow(landscape)
plt.show()

[[-2.98950005 -2.99825001 -2.98300004 ... -3.01149988 -2.99799991
  -3.00200009]
 [-2.9914999  -2.88474989 -2.78999996 ... -3.24950004 -3.14875007
  -3.04025006]
 [-2.98049998 -2.80349994 -2.55800009 ... -3.46125007 -3.30649996
  -3.10349989]
 ...
 [-2.99000001 -3.22900009 -3.4602499  ... -2.38875008 -2.59925008
  -2.91175008]
 [-2.98674989 -3.16149998 -3.30075002 ... -2.66100001 -2.76574993
  -2.91849995]
 [-3.02874994 -3.05725002 -3.08675003 ... -2.90324998 -2.94199991
  -2.95274997]]
In [9]:
# Mean of landscape
np.mean(landscape)
Out[9]:
-2.6386364763602614
In [10]:
# Minimium 
np.min(landscape)
Out[10]:
-3.9277501106262207
In [11]:
# Display Coordinates of Minimum 
np.unravel_index(np.argmin(landscape), landscape.shape)
Out[11]:
(26, 19)
In [12]:
# Gamma and beta value of Minimium
gamma, beta = np.unravel_index(np.argmin(landscape), landscape.shape)
opt_gamma = gamma * step_size
opt_beta = beta * step_size
print(f"Opt.Gamma: {opt_gamma}, Opt.Beta: {opt_beta}")

Opt.Gamma: 2.6, Opt.Beta: 1.9000000000000001
In [13]:
# Save result matrix 
with open('landscape_simulator_butterfly_no_weights_results.npy', 'wb') as f:
    np.save(f, landscape)
In [14]:
import plotly.graph_objects as go
In [15]:
# Plot landscape in 3D 
a_gamma = np.arange(0, np.pi, step_size)
b_beta = np.arange(0, np.pi, step_size)
fig = go.Figure(data=go.Surface(z=landscape, x=a_gamma, y=b_beta))

fig.update_traces(contours_z=dict(show=True, usecolormap=True, highlightcolor='limegreen', project_z=True))


fig.update_layout(title="QAOA MaxCut", scene=dict(
    xaxis_title="gamma",
    yaxis_title="beta",
    zaxis_title="mean"
))
In [16]:
# Plot Heatmap 
fig = go.Figure(data=go.Heatmap(z=landscape, x=b_beta, y=a_gamma, type = 'heatmap', colorscale = 'viridis'))

# Update Layout
fig.update_layout(title="QAOA MaxCut", width=700, height=700, xaxis_title="beta", yaxis_title="gamma")

# Display Global Minimium 
fig.add_trace(
    go.Scatter(mode="markers", x=[opt_beta], y=[opt_gamma], marker_symbol=[204], text = [landscape[gamma,beta]],
                   marker_color="red",  hovertemplate="x: %{x}<br>y: %{y}<br> z: %{text:.2f}<extra></extra>", 
                   marker_line_width=1, marker_size=16))
In [17]:
# Display Optimizer Results

# Display path 
#fig.add_trace(
#    go.Scatter(mode="lines", x=gammas, y=betas, marker_symbol=[200],
#                   marker_color="white", marker_line_width=1, marker_size=8)
#)

# Display start point
#fig.add_trace(
#    go.Scatter(mode="markers", x=[gammas[0]], y=[betas[0]], marker_symbol=[204],
#                   marker_color="gray", 
#                   marker_line_width=1, marker_size=16))

# Display end point
#fig.add_trace(
#    go.Scatter(mode="markers", x=[gammas[-1]], y=[betas[-1]], marker_symbol=[204],
#                   marker_color="green", 
#                   marker_line_width=1, marker_size=16))
In [18]:
# Plot Optimizer History
#fig = go.Figure(data=go.Scatter(x=counts, y=values))
#fig.update_layout(xaxis_title="Evaluation Counts", yaxis_title="Evaluated Mean", title="Optimizer")
#fig.show()